{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 代码参考  \n",
    "https://github.com/osh/KerasGAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "from keras.layers import Input\n",
    "from keras.layers.core import Reshape,Dense,Dropout,Activation,Flatten\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D, Deconv2D, UpSampling2D\n",
    "from keras.regularizers import *\n",
    "from keras.layers.normalization import *\n",
    "from keras.optimizers import *\n",
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.models import Model\n",
    "from tqdm import tqdm\n",
    "from IPython import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 1.0\n",
      "X_train shape: (60000, 1, 28, 28)\n",
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
    "X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "\n",
    "print(np.min(X_train), np.max(X_train))\n",
    "print('X_train shape:', X_train.shape)\n",
    "print(X_train.shape[0], 'train samples')\n",
    "print(X_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 超参数设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "shp = X_train.shape[1:]\n",
    "dropout_rate = 0.25\n",
    "\n",
    "# Optim优化器\n",
    "opt = Adam(lr=1e-4)\n",
    "dopt = Adam(lr=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义生成器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_31 (InputLayer)        (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_27 (Dense)             (None, 39200)             3959200   \n",
      "_________________________________________________________________\n",
      "batch_normalization_30 (Batc (None, 39200)             156800    \n",
      "_________________________________________________________________\n",
      "activation_39 (Activation)   (None, 39200)             0         \n",
      "_________________________________________________________________\n",
      "reshape_12 (Reshape)         (None, 200, 14, 14)       0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_12 (UpSampling (None, 200, 28, 28)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_45 (Conv2D)           (None, 100, 28, 28)       180100    \n",
      "_________________________________________________________________\n",
      "batch_normalization_31 (Batc (None, 100, 28, 28)       112       \n",
      "_________________________________________________________________\n",
      "activation_40 (Activation)   (None, 100, 28, 28)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_46 (Conv2D)           (None, 50, 28, 28)        45050     \n",
      "_________________________________________________________________\n",
      "batch_normalization_32 (Batc (None, 50, 28, 28)        112       \n",
      "_________________________________________________________________\n",
      "activation_41 (Activation)   (None, 50, 28, 28)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_47 (Conv2D)           (None, 1, 28, 28)         51        \n",
      "_________________________________________________________________\n",
      "activation_42 (Activation)   (None, 1, 28, 28)         0         \n",
      "=================================================================\n",
      "Total params: 4,341,425\n",
      "Trainable params: 4,262,913\n",
      "Non-trainable params: 78,512\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "K.set_image_dim_ordering('th')  # 用theano的图片输入顺序\n",
    "# 生成1 * 28 * 28的图片\n",
    "nch = 200\n",
    "g_input = Input(shape=[100])\n",
    "H = Dense(nch*14*14, kernel_initializer='glorot_normal')(g_input)\n",
    "H = BatchNormalization()(H)\n",
    "H = Activation('relu')(H)\n",
    "H = Reshape( [nch, 14, 14] )(H)  # 转成200 * 14 * 14\n",
    "H = UpSampling2D(size=(2, 2))(H)\n",
    "H = Convolution2D(100, (3, 3), padding=\"same\", kernel_initializer='glorot_normal')(H)\n",
    "H = BatchNormalization()(H)\n",
    "H = Activation('relu')(H)\n",
    "H = Convolution2D(50, (3, 3), padding=\"same\", kernel_initializer='glorot_normal')(H)\n",
    "H = BatchNormalization()(H)\n",
    "H = Activation('relu')(H)\n",
    "H = Convolution2D(1, (1, 1), padding=\"same\", kernel_initializer='glorot_normal')(H)\n",
    "g_V = Activation('sigmoid')(H)\n",
    "generator = Model(g_input,g_V)\n",
    "generator.compile(loss='binary_crossentropy', optimizer=opt)\n",
    "generator.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 定义辨别器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_34 (InputLayer)        (None, 1, 28, 28)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_50 (Conv2D)           (None, 256, 14, 14)       6656      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_22 (LeakyReLU)   (None, 256, 14, 14)       0         \n",
      "_________________________________________________________________\n",
      "dropout_22 (Dropout)         (None, 256, 14, 14)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_51 (Conv2D)           (None, 512, 7, 7)         3277312   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_23 (LeakyReLU)   (None, 512, 7, 7)         0         \n",
      "_________________________________________________________________\n",
      "dropout_23 (Dropout)         (None, 512, 7, 7)         0         \n",
      "_________________________________________________________________\n",
      "flatten_8 (Flatten)          (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "dense_30 (Dense)             (None, 256)               6422784   \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_24 (LeakyReLU)   (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dropout_24 (Dropout)         (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_31 (Dense)             (None, 2)                 514       \n",
      "=================================================================\n",
      "Total params: 9,707,266\n",
      "Trainable params: 9,707,266\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 辨别是否来自真实训练集\n",
    "d_input = Input(shape=shp)\n",
    "H = Convolution2D(256, (5, 5), activation=\"relu\", strides=(2, 2), padding=\"same\")(d_input)\n",
    "H = LeakyReLU(0.2)(H)\n",
    "H = Dropout(dropout_rate)(H)\n",
    "H = Convolution2D(512, (5, 5), activation=\"relu\", strides=(2, 2), padding=\"same\")(H)\n",
    "H = LeakyReLU(0.2)(H)\n",
    "H = Dropout(dropout_rate)(H)\n",
    "H = Flatten()(H)\n",
    "H = Dense(256)(H)\n",
    "H = LeakyReLU(0.2)(H)\n",
    "H = Dropout(dropout_rate)(H)\n",
    "d_V = Dense(2,activation='softmax')(H)\n",
    "discriminator = Model(d_input,d_V)\n",
    "discriminator.compile(loss='categorical_crossentropy', optimizer=dopt)\n",
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 构造生成对抗网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_33 (InputLayer)        (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "model_18 (Model)             (None, 1, 28, 28)         4341425   \n",
      "_________________________________________________________________\n",
      "model_19 (Model)             (None, 2)                 9707266   \n",
      "=================================================================\n",
      "Total params: 14,048,691\n",
      "Trainable params: 4,262,913\n",
      "Non-trainable params: 9,785,778\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 冷冻训练层\n",
    "def make_trainable(net, val):\n",
    "    net.trainable = val\n",
    "    for l in net.layers:\n",
    "        l.trainable = val\n",
    "make_trainable(discriminator, False)\n",
    "\n",
    "# 构造GAN\n",
    "gan_input = Input(shape=[100])\n",
    "H = generator(gan_input)\n",
    "gan_V = discriminator(H)\n",
    "GAN = Model(gan_input, gan_V)\n",
    "GAN.compile(loss='categorical_crossentropy', optimizer=opt)\n",
    "GAN.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 描绘损失收敛过程\n",
    "def plot_loss(losses):\n",
    "        display.clear_output(wait=True)\n",
    "        display.display(plt.gcf())\n",
    "        plt.figure(figsize=(10,8))\n",
    "        plt.plot(losses[\"d\"], label='discriminitive loss')\n",
    "        plt.plot(losses[\"g\"], label='generative loss')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "        \n",
    "        \n",
    "#  描绘生成器生成图像        \n",
    "def plot_gen(n_ex=16,dim=(4,4), figsize=(10,10) ):\n",
    "    noise = np.random.uniform(0,1,size=[n_ex,100])\n",
    "    generated_images = generator.predict(noise)\n",
    "\n",
    "    plt.figure(figsize=figsize)\n",
    "    for i in range(generated_images.shape[0]):\n",
    "        plt.subplot(dim[0],dim[1],i+1)\n",
    "        img = generated_images[i,0,:,:]\n",
    "        plt.imshow(img)\n",
    "        plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 288s 14ms/step - loss: 0.0469\n"
     ]
    }
   ],
   "source": [
    "# 抽取训练集样本\n",
    "ntrain = 10000\n",
    "trainidx = random.sample(range(0,X_train.shape[0]), ntrain)\n",
    "XT = X_train[trainidx,:,:,:]  \n",
    "\n",
    "# 预训练辨别器\n",
    "noise_gen = np.random.uniform(0,1,size=[XT.shape[0],100])\n",
    "generated_images = generator.predict(noise_gen)  # 生成器产生样本\n",
    "X = np.concatenate((XT, generated_images))  \n",
    "n = XT.shape[0]\n",
    "y = np.zeros([2*n,2])  # 构造辨别器标签 one-hot encode\n",
    "y[:n,1] = 1\n",
    "y[n:,0] = 1\n",
    "\n",
    "make_trainable(discriminator,True)\n",
    "discriminator.fit(X,y, epochs=1, batch_size=32)\n",
    "y_hat = discriminator.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000 of 20000) right\n"
     ]
    }
   ],
   "source": [
    "#  计算辨别器的准确率\n",
    "y_hat_idx = np.argmax(y_hat,axis=1)\n",
    "y_idx = np.argmax(y,axis=1)\n",
    "diff = y_idx-y_hat_idx\n",
    "n_total = y.shape[0]\n",
    "n_right = (diff==0).sum()\n",
    "\n",
    "print( \"(%d of %d) right\"  % (n_right, n_total))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 存储生成器和辨别器的训练损失\n",
    "losses = {\"d\":[], \"g\":[]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_for_n(nb_epoch=5000, plt_frq=25,BATCH_SIZE=32):\n",
    "    for e in tqdm(range(nb_epoch)):  \n",
    "        \n",
    "        # 生成器生成样本\n",
    "        image_batch = X_train[np.random.randint(0,X_train.shape[0],size=BATCH_SIZE),:,:,:]    \n",
    "        noise_gen = np.random.uniform(0,1,size=[BATCH_SIZE,100])\n",
    "        generated_images = generator.predict(noise_gen)\n",
    "        \n",
    "        # 训练辨别器\n",
    "        X = np.concatenate((image_batch, generated_images))\n",
    "        y = np.zeros([2*BATCH_SIZE,2])\n",
    "        y[0:BATCH_SIZE,1] = 1\n",
    "        y[BATCH_SIZE:,0] = 1\n",
    "        \n",
    "        # 存储辨别器损失loss\n",
    "        make_trainable(discriminator,True)\n",
    "        d_loss  = discriminator.train_on_batch(X,y)\n",
    "        losses[\"d\"].append(d_loss)  \n",
    "    \n",
    "        # 生成器生成样本\n",
    "        noise_tr = np.random.uniform(0,1,size=[BATCH_SIZE,100])\n",
    "        y2 = np.zeros([BATCH_SIZE,2])\n",
    "        y2[:,1] = 1\n",
    "        \n",
    "        # 存储生成器损失loss\n",
    "        make_trainable(discriminator,False)  # 辨别器的训练关掉\n",
    "        g_loss = GAN.train_on_batch(noise_tr, y2)\n",
    "        losses[\"g\"].append(g_loss)\n",
    "        \n",
    "        # 更新损失loss图\n",
    "        if e%plt_frq == plt_frq-1:\n",
    "            plot_loss(losses)\n",
    "            plot_gen()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      " 13%|█▎        | 130/1000 [14:59<1:42:30,  7.07s/it]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      " 13%|█▎        | 131/1000 [15:06<1:41:04,  6.98s/it]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      " 13%|█▎        | 132/1000 [15:12<1:40:22,  6.94s/it]\u001b[A\u001b[A\u001b[A"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-77-0ee3d9200fc0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_for_n\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnb_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplt_frq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-75-74136b7761fa>\u001b[0m in \u001b[0;36mtrain_for_n\u001b[0;34m(nb_epoch, plt_frq, BATCH_SIZE)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;31m# 存储辨别器损失loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mmake_trainable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdiscriminator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0md_loss\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mdiscriminator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m         \u001b[0mlosses\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"d\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1216\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1218\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "train_for_n(nb_epoch=1000, plt_frq=10,BATCH_SIZE=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 调参技巧    \n",
    "1.batch size  \n",
    "2.adam优化器的learning rate  \n",
    "3.迭代次数nb_epoch  \n",
    "4.生成器generat和辨别器discriminator的网络结构  "
   ]
  }
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